Through numerous interactions with computer systems in their day-to-day lives, users are exposed to a wide array of content, such communications (e.g., video, instant messaging, or interacting with a virtual agent) and various forms of digital media (e.g., internet sites, television shows, movies, augmented reality, and/or interactive computer games). Throughout these many interactions, affective computing systems can measure a user's affective response to the content and analyze the information. This analysis may provide insight that can be used to improve the current and/or future content presented to the user. For example, a computer game may be able to recognize if the difficulty level of the game is appropriate for the user, and adapt the game accordingly. In another example, a television may learn what programs a user is likely to enjoy from monitoring the user's reaction to previously shown programs. The television may then be able to select and deliver more appropriate content to the user, with essentially no need for the user to actively choose or intervene.
There are various sensors that may be utilized to measure a person's affective response to content. Examples of technologies that are found in an increasing number of consumer applications include various sensors that can measure physiological signals such as heart rate, blood-volume pulse, galvanic skin response (GSR), skin temperature, respiration, or brainwave activity such as electroencephalography (EEG).
Often, the sensors used to measure users are mobile and/or wireless (e.g., bracelets with GSR sensors, cameras, headsets with EEG sensors or implants); thus, they tend to require batteries for power. However, continually taking measurements of a user's affective response to content can severely drain the batteries. Thus, there is a need to reduce the power consumption of sensors used to measure affective response to content.